Statistical Methods for Improving Real-Time Public Health Surveillance and Integrated Outbreak Detection

项目摘要/摘要 新冠肺炎疫情突显了建立强有力的监测和监测系统的必要性。 为了及早发现和应对新出现的传染病，必须有强有力的分析 检查历史和当前数据以确定关键健康状况的潜在异常的工具 指标。当缺乏可靠的测试和报告数据时，尤其需要这样做。相反，关键字 相关指标，即死亡率和与感兴趣的疾病有关的症状，可以跟踪和 分析过了。存在两个问题：(1)报告延迟导致当前健康指标数据少算， 以及(2)先前的异常情况，如过去暴发导致的死亡率激增，扭曲了历史或基线数据。 因此，目标是开发方法，以进行持续的滚动监测和疫情检测 这两个问题的背景。 有两个由协作产生的大型数据集：(1)来自 波多黎各疾病控制和预防中心(CDC)和公共卫生部(DPH) 2017年1月至2021年12月，马萨诸塞州和加利福尼亚州的里科，以及(2)健康合作伙伴(PIH) 定期收集与新冠肺炎相关的健康管理信息系统数据 指标，特别是来自8个国家900个卫生机构的急性呼吸道感染(ARI) 2016年1月-目前。通过拟议研究计划的第一个目标，第一个数据集将是 分析以开发在当前数据中计算不足的方法。在这样做的过程中，各种 将解决现有研究中的方法差距，包括考虑#年的季节性 报告滞后模式，并提供有关估计的不确定性测量。通过第二个目标 将分析拟议的研究计划、第二个数据集以及模拟版本，以 通过同时解决两个差距来制定滚动疫情检测方法： 以前的数据偏差和优化关键统计属性，包括偏差、方差和适当 模特合身。 这两个目标在传染病监测中是相辅相成的，同样重要。而当 将分析上述特定的数据集和指标，开发的方法将是 广泛适用于任何关键健康指标的监测。随着与新冠肺炎相关的挑战持续存在和 尽管出现了新的威胁，但在统计上严谨的早期检测工具仍然至关重要。只是 同样重要的是在可访问的、易于使用的开源软件包中传播这些工具， 建议的研究计划的关键方面。

Project Summary/Abstract The COVID-19 pandemic has accentuated the need for strong monitoring and surveillance systems. To conduct early detection and response to emerging infectious diseases, there must be robust analytical tools that examine historical and current data in order to identify potential aberrations in key health indicators. This is especially needed when reliable testing and reporting data is lacking. Instead, key associated indicators, namely mortality and related symptoms to a disease of interest, can be tracked and analyzed. Two problems exist: (1) reporting delays lead to undercounts in current health indicators data, and (2) prior anomalies such as spikes in mortality due to past outbreaks distort historical or baseline data. Thus, the goal is to develop methods to conduct ongoing, rolling surveillance and outbreak detection in the context of these two issues. Two large datasets resulting from collaborations are available: (1) state-level mortality data from the Centers for Disease Control and Prevention (CDC) and Departments of Public Health (DPH) in Puerto Rico, Massachusetts, and California from January 2017-December 2021, and (2) Partners in Health (PIH) routinely collected health management information systems (HMIS) data on COVID-19-associated indicators, specifically acute respiratory infections (ARI) from 900 health facilities in 8 countries from January 2016-current. Through the first aim of the proposed research plan, the first dataset will be analyzed to develop methods for imputing undercounts in current data. In doing so, various methodological gaps in existing research will be addressed, including accounting for seasonality in reporting lag patterns and providing measures of uncertainty around estimates. Through the second aim of the proposed research plan, the second dataset set, along with a simulated version, will be analyzed to develop methods for rolling outbreak detection by simultaneously addressing two gaps: accounting for prior data aberrations and optimizing key statistical properties including bias, variance, and appropriate model fit. Both goals are complementary and equally important in infectious disease surveillance. While the specific datasets and indicators as described above will be analyzed, the developed methods will be broadly applicable to monitoring of any key health indicators. As COVID-19-related challenges persist and new threats emerge, statistically rigorous tools for early detection remain of paramount importance. Just as important is dissemination of these tools in accessible, easily usable open-source software packages, a key aspect of the proposed research plan.